At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-
Fixability, and Approximation” that won the conference's best paper award. In this technical …

This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a
pioneering algorithm of the density-based clustering technique. It provides the ability to …

High-dimensional data arise naturally in many domains, and have regularly presented a
great challenge for traditional data mining techniques, both in terms of effectiveness and …

This paper documents the release of the ELKI data mining framework, version 0.7. 5. ELKI is
an open source (AGPLv3) data mining software written in Java. The focus of ELKI is …

In today's world, where we generate large amount of data, we can harness the benefits of
the hidden information ie patterns or correlations in these data. This information can be used …

The physical condition of American infrastructure systems has raised concerns that have
been addressed, in part, by studies addressing their condition assessment. Condition …

Abstract Advances in Online Social Networks is creating huge data day in and out providing
lot of opportunities to its users to express their interest and opinion. Due to the popularity …

Clustering is a classical research field due to its broad applications in data mining such as
emotion detection, event extraction and topic discovery. It aims to discover intrinsic patterns …

The problem of clustering climate data observation sites and grouping them by their climate
types is considered. Machine learning–based clustering algorithms are used in analyzing …